Typically, discriminative SSL pretext tasks rely on data augmentations/transformations. In computer vision domain,
classic effective spatial transformation sets include image colorization [1]}, [2]}, image affine transformations e.g. rotations [3]}, [4]}, and other geometric transformations e.g. jigsaw transformations [1]}, [6]}.
In video domain, there are also temporal transformation sets, including sorting sequences [7]}, [8]}, [9]}, future prediction [10]}, [11]}, [12]}, and tracking [13]}. A detailed study of which data augmentation works the best for video self-supervised learning can be found in [14]}.
